Abstract
Extreme Learning Machine (ELM) is a novel learning algorithm for Neural Networks (NN) much faster than the traditional gradient-based learning techniques, and many variants, extensions and applications in the NN field have been appeared in the recent literature. Among them, an ELM approach has been applied to training Time-Variant Neural Networks (TV-NN), with the main objective to reduce the training time. Moreover, interesting approaches have been proposed to automatically determine the number of hidden nodes, which represents one of the limitations of original ELM algorithm for NN. In this paper, we extend the Error Minimized Extreme Learning Machine (EM-ELM) algorithm along with other two incremental based ELM methods to the time-variant case study, which is actually missing in the related literature. Comparative simulation results show the the proposed EM-ELM-TV is efficient to optimally determine the basic network architecture guaranteeing good generalization performances at the same time.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proc. IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990 (2004)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1-3), 489–501 (2006)
Huang, G., Chen, L., Siew, C.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 17(4), 879 (2006)
Huang, G., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16-18), 3460–3468 (2008)
Feng, G., Huang, G.B., Lin, Q., Gay, R.: Error minimized extreme learning machine with growth of hidden nodes and incremental learning 20(8), 1352–1357 (2009)
Titti, A., Squartini, S., Piazza, F.: A new time-variant neural based approach for nonstationary and non-linear system identification. In: Proc. IEEE International Symposium on Circuits and Systems, ISCAS, pp. 23–26, 5134–5137 (2005)
Cingolani, C., Squartini, S., Piazza, F.: An extreme learning machine approach for training time variant neural networks. In: Proc. IEEE Asia Pacific Conference on Circuits and Systems, APCCAS, pp. 384–387 (2008)
Narendra, K., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1(1), 4–27 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ye, Y., Squartini, S., Piazza, F. (2010). Incremental-Based Extreme Learning Machine Algorithms for Time-Variant Neural Networks. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-14922-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14921-4
Online ISBN: 978-3-642-14922-1
eBook Packages: Computer ScienceComputer Science (R0)